Smartphone Based Context Flow Recognition For Outdoor Parking System


Hossen, Md Ismail (2019) Smartphone Based Context Flow Recognition For Outdoor Parking System. Masters thesis, Multimedia University.

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Outdoor parking system is one of the most crucial needs for smart cities to find the occupancy of parking in outdoor environments such as roadsides, university campus, and so on. Currently, there are many camera-based and external sensors-based parking systems available. The camera-based parking systems rely on camera set-up which is sophisticated, while sensors-based parking systems require installation of sensors at the parking spots or vehicles. Due to such complication, the deployment and maintenance costs of the existing parking systems are very high. Besides, the need for additional hardware and networks increases the cost and complexity which makes it difficult to use in outdoor environments. The objective of this research is to design a method to automatically detect the flow of a driver’s context for outdoor parking or unparking actions by taking advantage of the rapid deployment of smartphones. The proposed method has three major components, which are (1) signal pre-processing, (2) context recognition, and (3) context flow recognition. The input signals received from the user’s phone are preprocessed to prepare the raw input for further processing. After that, the context recognition component recognises the contexts of drivers. Lastly, context flow recognition detects the flows of activities to conclude whether the driver is parking or leaving the parking place. The driver’s activit ies flow like walking → idle→ driving, walking → driving tells whether the driver is leaving the parking space or parking his/her vehicle.

Item Type: Thesis (Masters)
Additional Information: Call No.: HE336.P37 M35 2019
Uncontrolled Keywords: Automobile parking
Subjects: H Social Sciences > HE Transportation and Communications > HE1-9990 Transportation and communications (General) > HE331-380 Traffic engineering. Roads and highways. Streets
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 21 Sep 2020 20:38
Last Modified: 21 Sep 2020 20:38


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